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#30DayMapChallenge 19 | Urban Travel speeds in Greater London (movement data from Uber Open Source, with some #rstats and #rspatial to clean it up)

#30DayMapChallenge 20 | Rural Croplands across the globe, measured by intensity within square kilometer grid cells—37% of total land (made in #QGIS with data from IIASA Geo-Wiki's application geo-wiki.org)

Stumbled into #30DayMapChallenge 25 | Climate during some other work Mean daytime temperature for the United States—and visible BosWash urban heat island (with #EarthEngine and some help from this Justin Braaten tutorial developers.google.com/earth-engine/t…)

Started out wondering how to define urban agglomerations; ended up mapping 30 million commutes and accidentally copying some nyx and Alasdair Rae work doing just that (journals.plos.org/plosone/articl…)—quite a Sunday. Credit @underdarkGIS for the effect.

A while ago, I saw a tweet from Francisco Rowe ✌️ (since lost) on lights, population and buildings to capture development. Whatever happened to the project? Had time to replicate (I think) with an interactive #EarthEngine (#GEE) map—and it is fascinating. (See asrenninger.users.earthengine.app/view/dimension…)

Hat off to Geoff Boeing for letting anyone reproduce his work—quite easily—with OSMnx; on a whim, looked at the entropy of each road network of all refugee camps on OpenStreetMap. Inspired by Richard Davies on Zaatari and Azraq in Extreme Economies (see geoffboeing.com/2016/11/osmnx-…)

#30DayMapChallenge | Points Everywhere with "point" in the name according to the USGS (usgs.gov/core-science-s…), and then broken up by adjacent features.

#30DayMapChallenge | Lines Comparing a (rather random) collection of transit networks using data from Rainer Kujala et al (nature.com/articles/sdata…).

#30DayMapChallenge | Connections Bay Area travel patterns over 2020 using data from SafeGraph; connections still down substantially from January or February.

Saturday morning project: Lloyd's algorithm assisted by #rstats and #rspatial. Thanks for the introduction Matt Dzugan; not quite getting to perfect regularity but working with a larger area. #generative

Absolutely loving the osmextract #rstats package; #rspatial keeps getting better. Great work from Robin Lovelace @[email protected] and Andrea Gilardi. For the uninitiated though (me an hour ago), hstore_get_value is the key to any good where clause—maybe include in the vignette?

This article has the most PennMUSA call to arms Ken Steif. The most promising Smart Cities companies are "smaller companies that are coming up with pinpointed solutions to very distinct, often urgent problems." Targeted solutions from domain experts. citymonitor.ai/government/why…

Philadelphia buses during the morning commute (or what is left of it); accessibility made easy by the gtfs2gps package in #rstats thanks to Rafael H. M. Pereira 🚡 Urban Demographics and Instituto de Pesquisa Econômica Aplicada. The #rspatial ecosystem has a package for everything.

The geography of startups. With limited data, this firefly technique from Dr. Dominic Royé adds an interesting effect (dominicroye.github.io/en/2021/firefl…) Data scraped from wikipedia in #rstats #rspatial

#30DayMapChallenge | Points H1B visa applications in the United States. College Station, Texas, really needed to import talent in 2015? Made with #rstats and #rspatial

#30DayMapChallenge | Lines Monthly flights according to OpenSky (zenodo.org/record/4737390…) since January 2020. #rstats #rspatial

#30DayMapChallenge | Polygons All of the coordinate reference systems in Kyle Walker's excellent crssuggest #rstats package (projected in World Robinson, of course). #rspatial

#30DayMapChallenge | Hexagons The most scenic areas in Britain according to Scenic or Not (scenicornot.datasciencelab.co.uk) and Data Science Lab. Easily done in #rstats and #rspatial with this code (github.com/asrenninger/ti…)

#30DayMapChallenge | OSM The downtown (according to Latitude Longitude) of the largest city in the largest countries, made with OSMnx and #Python.

#30DayMapChallenge | Red + Green In the name of science, I ran 40 miles along the London canals and joined it to air pollution data (from londonair.org.uk/london/asp/ann…) to see how bad it is for my lungs. #rstats #rspatial